RiffSalad is a local-first app Jeswin Jaison built to help his guitarist friend Sam find saved ideas by musical memory instead of cryptic filenames. A search such as “heavy, Drop C, around 145, I think it was a chorus idea” can draw on audio analysis, searchable voice notes, and semantic similarity. In Jaison’s account, the system combines conventional measurements with open models running on the user’s machine; its musical estimates are useful clues, not guaranteed facts.
Why build a riff vault?
Guitar ideas are often recorded quickly and named poorly—or not named at all. Later, a player may remember the tuning, mood, approximate tempo, or where the idea might fit, but not the filename. RiffSalad is designed around that kind of recall: instead of browsing recordings one by one, Sam can search for phrases such as “heavy drop C riff around 140 BPM” or “clean mellow fingerpicked thing in E minor.” These are examples from Jaison’s account, not results from a user survey.
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The project accepts imported WAV, MP3, and M4A files as well as recordings made in the browser. It analyzes the audio and makes musical descriptions and voice-note context searchable. Jaison describes the project and its implementation in his first-person article on DEV Community.
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How the audio and AI pipeline works
Rather than asking one AI model to listen to a recording and make every decision, the design combines audio-analysis tools, a small language model, and ordinary search logic. The figures and implementation details below are Jaison’s account; they have not been independently benchmarked here.
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Audio analysis extracts retrieval clues
Jaison says librosa measures BPM, key, energy, note density, and duration. For tempo, the described process separates harmonic and percussive components before tracking tempo; key detection compares tonal profiles. A Basic Pitch-style ONNX model is used to extract MIDI. These features give the search system concrete attributes to filter or compare, but they are not a definitive transcription of what a guitarist played.
Voice notes add searchable context
For spoken memos, the project uses the tiny model from faster-whisper to transcribe audio locally. That lets a player search words spoken around the time an idea was recorded, alongside tags, titles, and musical descriptions.
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A small local model interprets the query
Ollama serves a 1.5-billion-parameter Qwen2.5 model to turn natural-language queries into structured candidates such as BPM ranges, keys, energy, and mood words. The project also stores nomic-embed-text embeddings alongside a SQLite database. FastAPI and SQLAlchemy power the back end; the front end uses React 19, TypeScript, Vite, Tailwind, and WaveSurfer.js.
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- Parse the phrase: The language model converts the text into candidate filters—for example, a tempo range, a key, or descriptive mood terms.
- Apply measurable constraints: Ordinary application code removes recordings that conflict with measurable requirements, rather than trusting the language model to decide a subjective label such as “heavy.”
- Rerank by meaning: Embedding similarity ranks the remaining candidates by how closely their searchable descriptions and context match the query.
- Fall back when needed: If the model or embeddings are unavailable, text matching still searches titles, notes, tags, transcripts, and descriptions.
For “find similar,” Jaison says RiffSalad blends embedding similarity with distance between measurable features such as BPM, key, and energy. The architectural idea is to use hard-to-argue-with measurements to narrow the field, then let similarity help order plausible matches—not to let a language model invent certainty about the music.
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What the local-first design means
Jaison says recordings, transcripts, and the database remain in a data/ folder on the user’s machine. He also says the first run downloads models and that the project then works without internet. That is a description of this project’s reported setup, not a guarantee that every dependency, installation method, or deployment will behave identically offline.
Keeping the working library local means the user is responsible for protecting it. A separate backup—such as a portable external drive or another chosen storage method—can help guard against device loss or failure. No particular drive, capacity, brand, or medium is required by the project, and none is endorsed here.
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Limits to keep in mind
- Key and tempo are estimates: Jaison explicitly warns that key and BPM detection are heuristics and can be wrong. Both can be overridden inline, so treat them as editable search clues rather than authoritative analysis.
- Speed is a single reported result: Jaison says a riff took less than 15 seconds to analyze on an M4 MacBook Air. He gives no benchmark method or hardware comparison, so that figure should not be generalized to other machines or libraries.
- Accuracy is not quantified: The article supplies no test-set size, measured accuracy, or independent privacy audit. It explains the intended design and reports how it behaved for the author, but does not establish comparative performance against hosted AI systems.
What the project demonstrates
RiffSalad addresses a narrow but practical retrieval problem: a guitarist remembers how an idea felt, not what its file was called. Its design pairs explicit musical measurements with local language and embedding tools, while retaining a text-search fallback. The most important practical detail is that automatic analysis is an aid to organizing a personal library; players can correct uncertain musical attributes rather than treating model output as ground truth.
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